Nonparametric density estimation based on the truncated mean

被引：1
|
作者：
Zhu, Ying [1 ]
机构：
[1] Univ Calif Berkeley, Haas Sch Business, Berkeley, CA 94720 USA
来源：
STATISTICS & PROBABILITY LETTERS | 2013年 / 83卷 / 02期
关键词：
Nonparametric density estimation; Check function; Kernel estimation;
D O I：
10.1016/j.spl.2012.10.023
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
Motivated by the optimality condition of a quantile loss minimization problem, a new family of closed-form density estimators based on truncated means is developed and found to achieve smaller mean squared errors in estimating the tails of the normal and gamma distributions when compared to the symmetric Rosenblatt-Parzen kernel estimator. (C) 2012 Elsevier B.V. All rights reserved.
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页码：445 / 451
页数：7
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